GMTplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1fffd8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os \n",
    "%matplotlib inline\n",
    "from datetime import timedelta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19873b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddir= '/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/postprocessing/'\n",
    "#read in different files\n",
    "def open_ds(var,case,esm='cesm',model='cam',ens='001',stream='h0'):\n",
    "    if esm=='cesm':\n",
    "        if case[:4]=='hist':\n",
    "            comp='BHIST'\n",
    "            tseriesdir = ddir + 'b.e213.'+comp+'_BPRP.f09_g17.esm-hist-i308.'+ens+'/' \n",
    "        else:\n",
    "            comp='BSSP126'\n",
    "            tseriesdir = ddir + 'b.e213.'+comp+'_BPRP.f09_g17.esm-'+case+'-i308.'+ens+'/' \n",
    "\n",
    "        # define filename\n",
    "        fn = var+'_b.e213.'+comp+'_BPRP.f09_g17.esm-'+case+'-i308.'+ ens + '.' + model + '.' + stream + '.nc'\n",
    "    else:\n",
    "        ens='i'+ens[-1]\n",
    "\n",
    "        if case=='hist':\n",
    "            case='histctl'\n",
    "            tspan='1980-2014'\n",
    "        else:\n",
    "            tspan='2015-2099'\n",
    "        tseriesdir = ddir + esm+'/'+case+'/'\n",
    "        # define filename\n",
    "        if stream=='h0':\n",
    "            fn = var+'_monthly_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "        elif stream=='h2':\n",
    "            fn = var+'_3hr_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "        elif stream=='h1':\n",
    "            fn = var+'_'+esm+'_'+case+'_'+ens+'_no-dynveg_'+tspan+ '.nc'\n",
    "\n",
    "    # check if variable timeseries exists and open variable as data array\n",
    "    if not os.path.isfile(tseriesdir + fn):\n",
    "        print(fn + ' does not exists in ')\n",
    "        print(tseriesdir)\n",
    "        return\n",
    "    else: \n",
    "        \n",
    "        if esm=='cesm':\n",
    "            ds = xr.open_dataset(tseriesdir+fn)\n",
    "        else:\n",
    "           # ds = xr.open_dataset(savedir+fn)\n",
    "            #print(ds.time.attrs['units'])\n",
    "            ds = xr.open_dataset(tseriesdir+fn)\n",
    "            ds['time']=pd.to_datetime(ds['time'], format='%Y%m%d') \n",
    "        return ds\n",
    "\n",
    "def open_da(var, case,esm='cesm', model='cam',ens='001',stream='h0'):\n",
    "    ds = open_ds(var, case, esm,model,ens,stream)\n",
    "    if var=='2t':\n",
    "        var='var167'\n",
    "    da = ds[var]\n",
    "    return da\n",
    "\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('TREFHT','hist','cesm','cam','001')\n",
    "ds_TREFHT_hist_2=open_da('TREFHT','hist','cesm','cam','002')\n",
    "ds_TREFHT_hist_3=open_da('TREFHT','hist','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('TREFHT','futctl','cesm','cam','001')\n",
    "ds_TREFHT_ctl_2=open_da('TREFHT','futctl','cesm','cam','002')\n",
    "ds_TREFHT_ctl_3=open_da('TREFHT','futctl','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('TREFHT','futsust','cesm','cam','001')\n",
    "ds_TREFHT_sust_2=open_da('TREFHT','futsust','cesm','cam','002')\n",
    "ds_TREFHT_sust_3=open_da('TREFHT','futsust','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('TREFHT','futineq','cesm','cam','001')\n",
    "ds_TREFHT_ineq_2=open_da('TREFHT','futineq','cesm','cam','002')\n",
    "ds_TREFHT_ineq_3=open_da('TREFHT','futineq','cesm','cam','003')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('tas','hist','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('tas','hist','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('tas','hist','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('tas','futctl','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('tas','futctl','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('tas','futctl','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('tas','futsust','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('tas','futsust','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('tas','futsust','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('tas','futineq','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('tas','futineq','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('tas','futineq','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('tas','hist','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('tas','hist','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('tas','hist','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('tas','futctl','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('tas','futctl','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('tas','futctl','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('tas','futsust','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('tas','futsust','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('tas','futsust','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('tas','futineq','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('tas','futineq','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('tas','futineq','ecearth','cam','003')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b319d280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM\n",
      "futctl-presctl\n",
      "1.0532733\n",
      "0.5481718\n",
      "(1.020580459965556, 1.0859661804305623)\n",
      "3.8909804389673486e-176\n",
      "sust-futctl\n",
      "-0.06718987\n",
      "0.38216642\n",
      "(-0.08998220267998902, -0.044397541849735093)\n",
      "3.7641412455985755e-09\n",
      "ineq-futctl\n",
      "0.2885674\n",
      "0.3772169\n",
      "(0.2660702514190703, 0.3110645366172761)\n",
      "1.3847166665150744e-95\n",
      "sust-ineq\n",
      "-0.3557573\n",
      "0.3875658\n",
      "(-0.37887164424029685, -0.3326429479304185)\n",
      "4.014890284327222e-118\n",
      "EC-EARTH\n",
      "futctl-presctl\n",
      "1.0202262\n",
      "0.5320006\n",
      "(0.9884978281485798, 1.0519546521675824)\n",
      "5.120493780991088e-177\n",
      "sust-futctl\n",
      "0.06287048\n",
      "0.37334228\n",
      "(0.040604419682166756, 0.08513654055819732)\n",
      "2.8456615500884428e-08\n",
      "ineq-futctl\n",
      "0.29585674\n",
      "0.37143525\n",
      "(0.27370441796164124, 0.318009070140318)\n",
      "1.4351901859470899e-100\n",
      "sust-ineq\n",
      "-0.23298629\n",
      "0.31080747\n",
      "(-0.25152278291246655, -0.2144497896526122)\n",
      "4.0584034620180575e-92\n",
      "MPI-ESM\n",
      "futctl-presctl\n",
      "0.3529427\n",
      "0.44987226\n",
      "(0.3261124116661227, 0.3797729986427152)\n",
      "6.293477297444989e-96\n",
      "sust-futctl\n",
      "0.06652103\n",
      "0.34791067\n",
      "(0.04577170905661962, 0.0872703582327328)\n",
      "7.320949308954592e-09\n",
      "ineq-futctl\n",
      "0.16996805\n",
      "0.36057687\n",
      "(0.1484633196756095, 0.19147278772146997)\n",
      "2.4301628339436917e-46\n",
      "sust-ineq\n",
      "-0.10344702\n",
      "0.3483319\n",
      "(-0.12422146647980162, -0.08267257362792543)\n",
      "1.4993437054368913e-21\n"
     ]
    }
   ],
   "source": [
    "##compute statistical significance of differences\n",
    "#1) pool all ensemble members\n",
    "#2) take spatial mean\n",
    "#3) compute differences, stdev and significance\n",
    "\n",
    "from scipy import stats\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_hist_2.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_hist_3.mean(['lat','lon'])[-30*12-1:-1]],'time')\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_ineq_2.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_ineq_3.mean(['lat','lon'])[-30*12-1:-1]],'time')\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_sust_2.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_sust_3.mean(['lat','lon'])[-30*12-1:-1]],'time')\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_ctl_2.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_ctl_3.mean(['lat','lon'])[-30*12-1:-1]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values\n",
    "diff_sust=ds_TREFHT_sust_mean.values-ds_TREFHT_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_ineq_mean.values-ds_TREFHT_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_sust_mean.values-ds_TREFHT_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('CESM')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_hist_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_hist_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_ineq_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_ineq_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_sust_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_sust_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_ctl_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_ecearth_ctl_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values\n",
    "diff_sust=ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('EC-EARTH')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)\n",
    "    \n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_hist_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_hist_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_sust_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_sust_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon'])[-30*12:],ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon'])[-30*12:]],'time')\n",
    "\n",
    "diff_ctl=ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values\n",
    "diff_sust=ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_ctl_mean.values\n",
    "diff_ineq=ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_ctl_mean.values\n",
    "diff_lcc=ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_ineq_mean.values\n",
    "\n",
    "diff_list=[diff_ctl,diff_sust,diff_ineq,diff_lcc]\n",
    "case_list=['futctl-presctl','sust-futctl','ineq-futctl','sust-ineq']\n",
    "print('MPI-ESM')\n",
    "for i in range(len(diff_list)):\n",
    "    print(case_list[i])\n",
    "    print(np.mean(diff_list[i]))\n",
    "    print(np.std(diff_list[i]))\n",
    "    n = len(diff_list[i])\n",
    "    std_err = np.std(diff_list[i]) / np.sqrt(n)\n",
    "    ci = stats.norm.interval(0.95, loc=np.mean(diff_list[i]), scale=std_err)    \n",
    "    print(ci)\n",
    "    jos,pvalue= stats.wilcoxon(diff_list[i])\n",
    "    print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00b7e144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'TREFHT' (time: 360)>\n",
      "array([277.11044, 276.81653, 276.41556, 276.32852, 276.89252, 278.55817,\n",
      "       280.08835, 280.1525 , 280.0837 , 278.7823 , 278.09702, 277.33655,\n",
      "       277.02615, 276.67856, 275.797  , 275.71017, 276.97577, 279.08395,\n",
      "       280.2265 , 280.2711 , 280.32248, 279.12643, 277.8839 , 277.68744,\n",
      "       277.3018 , 277.27747, 276.68668, 276.3464 , 277.35913, 278.97662,\n",
      "       279.80493, 280.11087, 279.94995, 279.27017, 278.4724 , 278.04938,\n",
      "       277.43176, 276.94922, 276.3559 , 275.7847 , 277.52014, 278.95724,\n",
      "       279.8369 , 280.15875, 280.39966, 279.17203, 278.41675, 277.75803,\n",
      "       277.50748, 276.9619 , 276.7353 , 276.06943, 277.22748, 279.05292,\n",
      "       280.29437, 280.1321 , 280.1613 , 279.18774, 278.17908, 277.62234,\n",
      "       277.18472, 276.52017, 276.15555, 276.2594 , 277.3494 , 279.15897,\n",
      "       280.00375, 280.0615 , 279.98578, 279.41586, 278.12906, 277.1772 ,\n",
      "       277.1098 , 276.87796, 275.94498, 275.5292 , 277.02615, 278.84915,\n",
      "       280.13416, 280.12683, 280.2778 , 279.27   , 278.28946, 277.35138,\n",
      "       277.2525 , 277.09402, 275.7745 , 275.89188, 277.17444, 279.13196,\n",
      "       279.92722, 280.51776, 279.91687, 278.77682, 278.41757, 277.6739 ,\n",
      "       277.5399 , 276.76422, 276.13324, 276.739  , 277.5793 , 279.25815,\n",
      "       280.34668, 280.69772, 280.43427, 279.53235, 278.21927, 277.5006 ,\n",
      "       277.3095 , 277.39734, 276.9849 , 275.7764 , 277.79343, 279.11877,\n",
      "       280.3984 , 281.12317, 280.49658, 279.69388, 278.63388, 278.92398,\n",
      "...\n",
      "       280.62094, 280.88614, 280.7134 , 279.78494, 279.02768, 278.15695,\n",
      "       277.79868, 277.34796, 277.25055, 277.01144, 278.04077, 279.6139 ,\n",
      "       280.6998 , 280.53284, 280.1722 , 279.82233, 279.1434 , 278.48236,\n",
      "       278.30594, 277.7105 , 276.86203, 276.67056, 278.18503, 279.60052,\n",
      "       280.83652, 281.24744, 280.6815 , 279.92615, 279.16882, 278.52362,\n",
      "       278.15414, 277.55676, 277.1462 , 277.10123, 277.92172, 279.1931 ,\n",
      "       280.58237, 280.68942, 280.6094 , 280.0516 , 279.00394, 278.1859 ,\n",
      "       278.4207 , 277.40463, 276.76434, 276.52747, 278.12457, 279.59473,\n",
      "       280.81412, 280.76672, 280.96793, 279.8577 , 279.41107, 278.83975,\n",
      "       278.01736, 277.87827, 277.52133, 277.20413, 278.02893, 279.60638,\n",
      "       280.78775, 281.01038, 280.73727, 280.07605, 279.3197 , 279.00363,\n",
      "       278.5442 , 277.8865 , 277.58228, 277.62347, 278.87698, 280.26306,\n",
      "       280.9934 , 281.28748, 280.6493 , 280.17584, 279.78397, 279.35696,\n",
      "       278.8674 , 278.35144, 277.384  , 277.65262, 278.5856 , 279.9254 ,\n",
      "       281.04138, 281.26907, 281.2638 , 280.2914 , 279.31403, 278.7387 ,\n",
      "       278.1528 , 277.6095 , 276.9223 , 276.59546, 278.30057, 279.96722,\n",
      "       281.06027, 281.1563 , 280.78015, 280.25922, 279.5698 , 279.2155 ,\n",
      "       279.0306 , 277.9568 , 277.35385, 277.62964, 278.61984, 279.64505,\n",
      "       280.99997, 281.23013, 280.72858, 280.5564 , 279.8894 , 279.57986],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * time     (time) object 1985-01-01 00:00:00 ... 2014-12-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1.mean(['lat','lon'])[-30*12-1:-1],ds_TREFHT_hist_2.mean(['lat','lon'])[-30:],ds_TREFHT_hist_3.mean(['lat','lon'])[-30:]],'time')\n",
    "print(ds_TREFHT_hist_1.mean(['lat','lon'])[-30*12-1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5998c9a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a31a8f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "083c3ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1_mean=ds_TREFHT_ecearth_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_hist_2_mean=ds_TREFHT_ecearth_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_hist_3_mean=ds_TREFHT_ecearth_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1_mean,ds_TREFHT_ecearth_hist_2_mean,ds_TREFHT_ecearth_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1_mean,ds_TREFHT_ecearth_ctl_2_mean,ds_TREFHT_ecearth_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1_mean=ds_TREFHT_ecearth_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_sust_2_mean=ds_TREFHT_ecearth_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_sust_3_mean=ds_TREFHT_ecearth_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1_mean,ds_TREFHT_ecearth_sust_2_mean,ds_TREFHT_ecearth_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1_mean,ds_TREFHT_ecearth_ineq_2_mean,ds_TREFHT_ecearth_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1_mean=ds_TREFHT_mpiesm_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_hist_2_mean=ds_TREFHT_mpiesm_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_hist_3_mean=ds_TREFHT_mpiesm_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1_mean,ds_TREFHT_mpiesm_hist_2_mean,ds_TREFHT_mpiesm_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1_mean,ds_TREFHT_mpiesm_ctl_2_mean,ds_TREFHT_mpiesm_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1_mean=ds_TREFHT_mpiesm_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_sust_2_mean=ds_TREFHT_mpiesm_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_sust_3_mean=ds_TREFHT_mpiesm_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1_mean,ds_TREFHT_mpiesm_sust_2_mean,ds_TREFHT_mpiesm_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-30:]\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1_mean,ds_TREFHT_mpiesm_ineq_2_mean,ds_TREFHT_mpiesm_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1_mean=ds_TREFHT_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_2_mean=ds_TREFHT_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_3_mean=ds_TREFHT_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1_mean,ds_TREFHT_hist_2_mean,ds_TREFHT_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ctl_1_mean=ds_TREFHT_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_2_mean=ds_TREFHT_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_3_mean=ds_TREFHT_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1_mean,ds_TREFHT_ctl_2_mean,ds_TREFHT_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_sust_1_mean=ds_TREFHT_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_2_mean=ds_TREFHT_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_3_mean=ds_TREFHT_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1_mean,ds_TREFHT_sust_2_mean,ds_TREFHT_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ineq_1_mean=ds_TREFHT_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_2_mean=ds_TREFHT_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_3_mean=ds_TREFHT_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1_mean,ds_TREFHT_ineq_2_mean,ds_TREFHT_ineq_3_mean],'time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b714c57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_ecearth_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "#    da_mask['lat']=da_delta.lat\n",
    "#    da_mask['lon']=da_delta.lon\n",
    "ds_TREFHT_ecearth_land_hist_1_mean=ds_TREFHT_ecearth_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_2_mean=ds_TREFHT_ecearth_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_3_mean=ds_TREFHT_ecearth_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_hist_mean=xr.concat([ds_TREFHT_ecearth_land_hist_1_mean,ds_TREFHT_ecearth_land_hist_2_mean,ds_TREFHT_ecearth_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_mean=xr.concat([ds_TREFHT_ecearth_land_ctl_1_mean,ds_TREFHT_ecearth_land_ctl_2_mean,ds_TREFHT_ecearth_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_sust_1_mean=ds_TREFHT_ecearth_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_2_mean=ds_TREFHT_ecearth_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_3_mean=ds_TREFHT_ecearth_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_mean=xr.concat([ds_TREFHT_ecearth_land_sust_1_mean,ds_TREFHT_ecearth_land_sust_2_mean,ds_TREFHT_ecearth_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_mean=xr.concat([ds_TREFHT_ecearth_land_ineq_1_mean,ds_TREFHT_ecearth_land_ineq_2_mean,ds_TREFHT_ecearth_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_mpiesm_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask['lat']=ds_TREFHT_mpiesm_hist_1.lat\n",
    "da_mask['lon']=ds_TREFHT_mpiesm_hist_1.lon\n",
    "\n",
    "ds_TREFHT_mpiesm_land_hist_1_mean=ds_TREFHT_mpiesm_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_2_mean=ds_TREFHT_mpiesm_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_3_mean=ds_TREFHT_mpiesm_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_mean=xr.concat([ds_TREFHT_mpiesm_land_hist_1_mean,ds_TREFHT_mpiesm_land_hist_2_mean,ds_TREFHT_mpiesm_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_mean=xr.concat([ds_TREFHT_mpiesm_land_ctl_1_mean,ds_TREFHT_mpiesm_land_ctl_2_mean,ds_TREFHT_mpiesm_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_sust_1_mean=ds_TREFHT_mpiesm_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_2_mean=ds_TREFHT_mpiesm_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_3_mean=ds_TREFHT_mpiesm_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_mean=xr.concat([ds_TREFHT_mpiesm_land_sust_1_mean,ds_TREFHT_mpiesm_land_sust_2_mean,ds_TREFHT_mpiesm_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_mean=xr.concat([ds_TREFHT_mpiesm_land_ineq_1_mean,ds_TREFHT_mpiesm_land_ineq_2_mean,ds_TREFHT_mpiesm_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_cesm_no_ice.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "\n",
    "ds_TREFHT_land_hist_1_mean=ds_TREFHT_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_2_mean=ds_TREFHT_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_3_mean=ds_TREFHT_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_mean=xr.concat([ds_TREFHT_land_hist_1_mean,ds_TREFHT_land_hist_2_mean,ds_TREFHT_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ctl_1_mean=ds_TREFHT_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_2_mean=ds_TREFHT_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_3_mean=ds_TREFHT_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_mean=xr.concat([ds_TREFHT_land_ctl_1_mean,ds_TREFHT_land_ctl_2_mean,ds_TREFHT_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_sust_1_mean=ds_TREFHT_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_2_mean=ds_TREFHT_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_3_mean=ds_TREFHT_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_mean=xr.concat([ds_TREFHT_land_sust_1_mean,ds_TREFHT_land_sust_2_mean,ds_TREFHT_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ineq_1_mean=ds_TREFHT_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_2_mean=ds_TREFHT_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_3_mean=ds_TREFHT_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_mean=xr.concat([ds_TREFHT_land_ineq_1_mean,ds_TREFHT_land_ineq_2_mean,ds_TREFHT_land_ineq_3_mean],'time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d1e1f8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da91956",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1d96d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1.5, 0, 'futctl-histctl'),\n",
       " Text(3.5, 0, 'inequality-histctl'),\n",
       " Text(5.5, 0, 'equality-histctl')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make bar plots (3 bars ctl-hist, ineq-ctl and sust-ctl with variability shown through min/max or boxplot?)\n",
    "data=[ds_TREFHT_ctl_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values]\n",
    "data1=[ds_TREFHT_ctl_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ctl_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_ctl_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_ctl_2_mean.values,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_ctl_3_mean.values]\n",
    "data3=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values,ds_TREFHT_sust_2_mean.values-ds_TREFHT_ctl_2_mean.values,ds_TREFHT_sust_3_mean.values-ds_TREFHT_ctl_3_mean.values]\n",
    "data4=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "data5=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_hist_1_mean.values,ds_TREFHT_sust_2_mean.values-ds_TREFHT_hist_2_mean.values,ds_TREFHT_sust_3_mean.values-ds_TREFHT_hist_3_mean.values]\n",
    "\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data4, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data5, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-histctl', 'equality-histctl'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c42b6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.2,2.2)\n",
    "ax2.set_ylim(0.58,2.98)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data2=[ds_TREFHT_ineq_1_mean.values-ds_TREFHT_ctl_1_mean.values+1,ds_TREFHT_ineq_2_mean.values-ds_TREFHT_ctl_2_mean.values+1,ds_TREFHT_ineq_3_mean.values-ds_TREFHT_ctl_3_mean.values+1]\n",
    "data3=[ds_TREFHT_sust_1_mean.values-ds_TREFHT_ctl_1_mean.values+1,ds_TREFHT_sust_2_mean.values-ds_TREFHT_ctl_2_mean.values+1,ds_TREFHT_sust_3_mean.values-ds_TREFHT_ctl_3_mean.values+1]\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['SSP1-1.9', 'inequality', 'sustainability'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax.hlines(y=1,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax.set_ylabel('GMT change to presctl')\n",
    "ax2.set_ylabel('GMT change to preindustrial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ce0a0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2871246337890625\n"
     ]
    }
   ],
   "source": [
    "print(np.median(data2[:])-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd08e148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to futctl')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.6,3)\n",
    "ax2.set_ylim(-0.85,2.75)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data1=[ds_TREFHT_mpiesm_ctl_1_mean.values-ds_TREFHT_mpiesm_hist_1_mean.values,ds_TREFHT_mpiesm_ctl_2_mean.values-ds_TREFHT_mpiesm_hist_2_mean.values,ds_TREFHT_mpiesm_ctl_3_mean.values-ds_TREFHT_mpiesm_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_mpiesm_ineq_1_mean.values-ds_TREFHT_mpiesm_ctl_1_mean.values+0.25,ds_TREFHT_mpiesm_ineq_2_mean.values-ds_TREFHT_mpiesm_ctl_2_mean.values+0.25,ds_TREFHT_mpiesm_ineq_3_mean.values-ds_TREFHT_mpiesm_ctl_3_mean.values+0.25]\n",
    "data3=[ds_TREFHT_mpiesm_sust_1_mean.values-ds_TREFHT_mpiesm_ctl_1_mean.values+0.25,ds_TREFHT_mpiesm_sust_2_mean.values-ds_TREFHT_mpiesm_ctl_2_mean.values+0.25,ds_TREFHT_mpiesm_sust_3_mean.values-ds_TREFHT_mpiesm_ctl_3_mean.values+0.25]\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-futctl', 'equality-futctl'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax2.set_ylabel('GMT change to futctl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76b3bfbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to futctl')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.6,3)\n",
    "ax2.set_ylim(-1.8,1.8)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "data1=[ds_TREFHT_ecearth_ctl_1_mean.values-ds_TREFHT_ecearth_hist_1_mean.values,ds_TREFHT_ecearth_ctl_2_mean.values-ds_TREFHT_ecearth_hist_2_mean.values,ds_TREFHT_ecearth_ctl_3_mean.values-ds_TREFHT_ecearth_hist_3_mean.values]\n",
    "data2=[ds_TREFHT_ecearth_ineq_1_mean.values-ds_TREFHT_ecearth_ctl_1_mean.values+1.2,ds_TREFHT_ecearth_ineq_2_mean.values-ds_TREFHT_ecearth_ctl_2_mean.values+1.2,ds_TREFHT_ecearth_ineq_3_mean.values-ds_TREFHT_ecearth_ctl_3_mean.values+1.2]\n",
    "data3=[ds_TREFHT_ecearth_sust_1_mean.values-ds_TREFHT_ecearth_ctl_1_mean.values+1.2,ds_TREFHT_ecearth_sust_2_mean.values-ds_TREFHT_ecearth_ctl_2_mean.values+1.2,ds_TREFHT_ecearth_sust_3_mean.values-ds_TREFHT_ecearth_ctl_3_mean.values+1.2]\n",
    "\n",
    "\n",
    "# first boxplot pair\n",
    "bp = ax.boxplot(data1, positions = [1, 1.5,2], widths = 0.4)\n",
    "\n",
    "# second boxplot pair\n",
    "bp = ax.boxplot(data2, positions = [3, 3.5,4], widths = 0.4)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp = ax.boxplot(data3, positions = [5, 5.5,6], widths = 0.4)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['futctl-histctl', 'inequality-futctl', 'equality-futctl'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax2.set_ylabel('GMT change to futctl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2944075e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial (°C)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-0.5,2.5)\n",
    "ax2.set_ylim(0.22,3.28)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_ineq_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_sust_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to presCTL (°C)')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futCTL', 'INEQ','SUST'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial (°C)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d34fcce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.0530453,mpiesm 0.35293987,ecearth 1.0202236\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f845beee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.3416127,mpiesm 0.5229116,ecearth 1.316081\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77040017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 0.9858531,mpiesm 0.41946343,ecearth 1.0830953\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73afe4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_land_ctl_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ctl_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ctl_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_land_ineq_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ineq_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ineq_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_land_sust_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_sust_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_sust_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('GMT change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futland_ctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3cb6523",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.584035,mpiesm 0.36705932,ecearth 1.130245\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52bcd2b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.845182,mpiesm 0.561872,ecearth 1.4087412\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc037114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.4800557,mpiesm 0.45736796,ecearth 1.1713209\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ece2d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('wbgt','hist','cesm','cam','001')\n",
    "ds_TREFHT_hist_2=open_da('wbgt','hist','cesm','cam','002')\n",
    "ds_TREFHT_hist_3=open_da('wbgt','hist','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('wbgt','futctl','cesm','cam','001')\n",
    "ds_TREFHT_ctl_2=open_da('wbgt','futctl','cesm','cam','002')\n",
    "ds_TREFHT_ctl_3=open_da('wbgt','futctl','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('wbgt','futsust','cesm','cam','001')\n",
    "ds_TREFHT_sust_2=open_da('wbgt','futsust','cesm','cam','002')\n",
    "ds_TREFHT_sust_3=open_da('wbgt','futsust','cesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('wbgt','futineq','cesm','cam','001')\n",
    "ds_TREFHT_ineq_2=open_da('wbgt','futineq','cesm','cam','002')\n",
    "ds_TREFHT_ineq_3=open_da('wbgt','futineq','cesm','cam','003')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('wbgt','hist','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('wbgt','hist','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('wbgt','hist','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('wbgt','futctl','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('wbgt','futctl','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('wbgt','futctl','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('wbgt','futsust','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('wbgt','futsust','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('wbgt','futsust','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('wbgt','futineq','mpiesm','cam','001')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('wbgt','futineq','mpiesm','cam','002')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('wbgt','futineq','mpiesm','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('wbgt','hist','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('wbgt','hist','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('wbgt','hist','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('wbgt','futctl','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('wbgt','futctl','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('wbgt','futctl','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('wbgt','futsust','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('wbgt','futsust','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('wbgt','futsust','ecearth','cam','003')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('wbgt','futineq','ecearth','cam','001')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('wbgt','futineq','ecearth','cam','002')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('wbgt','futineq','ecearth','cam','003')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85527c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1_mean=ds_TREFHT_ecearth_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_hist_2_mean=ds_TREFHT_ecearth_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_hist_3_mean=ds_TREFHT_ecearth_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "\n",
    "ds_TREFHT_ecearth_hist_mean=xr.concat([ds_TREFHT_ecearth_hist_1_mean,ds_TREFHT_ecearth_hist_2_mean,ds_TREFHT_ecearth_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ctl_mean=xr.concat([ds_TREFHT_ecearth_ctl_1_mean,ds_TREFHT_ecearth_ctl_2_mean,ds_TREFHT_ecearth_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1_mean=ds_TREFHT_ecearth_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_2_mean=ds_TREFHT_ecearth_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_3_mean=ds_TREFHT_ecearth_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_sust_mean=xr.concat([ds_TREFHT_ecearth_sust_1_mean,ds_TREFHT_ecearth_sust_2_mean,ds_TREFHT_ecearth_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_ineq_mean=xr.concat([ds_TREFHT_ecearth_ineq_1_mean,ds_TREFHT_ecearth_ineq_2_mean,ds_TREFHT_ecearth_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1_mean=ds_TREFHT_mpiesm_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_hist_2_mean=ds_TREFHT_mpiesm_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_hist_3_mean=ds_TREFHT_mpiesm_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_mean=xr.concat([ds_TREFHT_mpiesm_hist_1_mean,ds_TREFHT_mpiesm_hist_2_mean,ds_TREFHT_mpiesm_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ctl_mean=xr.concat([ds_TREFHT_mpiesm_ctl_1_mean,ds_TREFHT_mpiesm_ctl_2_mean,ds_TREFHT_mpiesm_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1_mean=ds_TREFHT_mpiesm_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_2_mean=ds_TREFHT_mpiesm_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_3_mean=ds_TREFHT_mpiesm_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_sust_mean=xr.concat([ds_TREFHT_mpiesm_sust_1_mean,ds_TREFHT_mpiesm_sust_2_mean,ds_TREFHT_mpiesm_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_ineq_mean=xr.concat([ds_TREFHT_mpiesm_ineq_1_mean,ds_TREFHT_mpiesm_ineq_2_mean,ds_TREFHT_mpiesm_ineq_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_hist_1_mean=ds_TREFHT_hist_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_2_mean=ds_TREFHT_hist_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_3_mean=ds_TREFHT_hist_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_hist_mean=xr.concat([ds_TREFHT_hist_1_mean,ds_TREFHT_hist_2_mean,ds_TREFHT_hist_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ctl_1_mean=ds_TREFHT_ctl_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_2_mean=ds_TREFHT_ctl_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_3_mean=ds_TREFHT_ctl_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ctl_mean=xr.concat([ds_TREFHT_ctl_1_mean,ds_TREFHT_ctl_2_mean,ds_TREFHT_ctl_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_sust_1_mean=ds_TREFHT_sust_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_2_mean=ds_TREFHT_sust_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_3_mean=ds_TREFHT_sust_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_sust_mean=xr.concat([ds_TREFHT_sust_1_mean,ds_TREFHT_sust_2_mean,ds_TREFHT_sust_3_mean],'time')\n",
    "\n",
    "ds_TREFHT_ineq_1_mean=ds_TREFHT_ineq_1.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_2_mean=ds_TREFHT_ineq_2.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_3_mean=ds_TREFHT_ineq_3.mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ineq_mean=xr.concat([ds_TREFHT_ineq_1_mean,ds_TREFHT_ineq_2_mean,ds_TREFHT_ineq_3_mean],'time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2484d1a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GMT change to preindustrial')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_ctl_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ctl_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ctl_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_ineq_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_ineq_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_ineq_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_sust_mean.values-ds_TREFHT_hist_mean.values,ds_TREFHT_mpiesm_sust_mean.values-ds_TREFHT_mpiesm_hist_mean.values,ds_TREFHT_ecearth_sust_mean.values-ds_TREFHT_ecearth_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('Global mean WBGT change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('GMT change to preindustrial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ed95d0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1216241457159608,mpiesm 0.3604595238730503,ecearth 0.9842903163544912\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db173e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.4125281374896215,mpiesm 0.5125076952808261,ecearth 1.2844391481957524\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4c021bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.0733757145189662,mpiesm 0.4380604513860293,ecearth 1.0494218535279862\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6e46dca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ds_TREFHT_hist_1=open_da('wbgt_monmax','hist',esm='cesm',model='clm2',ens='001',stream='h1')\n",
    "ds_TREFHT_hist_2=open_da('wbgt_monmax','hist','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_hist_3=open_da('wbgt_monmax','hist','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ctl_1=open_da('wbgt_monmax','futctl','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_ctl_2=open_da('wbgt_monmax','futctl','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_ctl_3=open_da('wbgt_monmax','futctl','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_sust_1=open_da('wbgt_monmax','futsust','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_sust_2=open_da('wbgt_monmax','futsust','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_sust_3=open_da('wbgt_monmax','futsust','cesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ineq_1=open_da('wbgt_monmax','futineq','cesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_ineq_2=open_da('wbgt_monmax','futineq','cesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_ineq_3=open_da('wbgt_monmax','futineq','cesm','clm2','003',stream='h1')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_hist_1=open_da('wbgt_monmax','hist','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_hist_2=open_da('wbgt_monmax','hist','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_hist_3=open_da('wbgt_monmax','hist','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_ctl_1=open_da('wbgt_monmax','futctl','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_ctl_2=open_da('wbgt_monmax','futctl','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_ctl_3=open_da('wbgt_monmax','futctl','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_sust_1=open_da('wbgt_monmax','futsust','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_sust_2=open_da('wbgt_monmax','futsust','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_sust_3=open_da('wbgt_monmax','futsust','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_mpiesm_ineq_1=open_da('wbgt_monmax','futineq','mpiesm','clm2','001',stream='h1')\n",
    "ds_TREFHT_mpiesm_ineq_2=open_da('wbgt_monmax','futineq','mpiesm','clm2','002',stream='h1')\n",
    "ds_TREFHT_mpiesm_ineq_3=open_da('wbgt_monmax','futineq','mpiesm','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_hist_1=open_da('wbgt_monmax','hist','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_hist_2=open_da('wbgt_monmax','hist','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_hist_3=open_da('wbgt_monmax','hist','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_ctl_1=open_da('wbgt_monmax','futctl','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_ctl_2=open_da('wbgt_monmax','futctl','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_ctl_3=open_da('wbgt_monmax','futctl','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_sust_1=open_da('wbgt_monmax','futsust','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_sust_2=open_da('wbgt_monmax','futsust','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_sust_3=open_da('wbgt_monmax','futsust','ecearth','clm2','003',stream='h1')\n",
    "\n",
    "ds_TREFHT_ecearth_ineq_1=open_da('wbgt_monmax','futineq','ecearth','clm2','001',stream='h1')\n",
    "ds_TREFHT_ecearth_ineq_2=open_da('wbgt_monmax','futineq','ecearth','clm2','002',stream='h1')\n",
    "ds_TREFHT_ecearth_ineq_3=open_da('wbgt_monmax','futineq','ecearth','clm2','003',stream='h1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e362a2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute spatial means (global and/or regional)\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_ecearth.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask=da_mask.mean('time')\n",
    "#    da_mask['lat']=da_delta.lat\n",
    "#    da_mask['lon']=da_delta.lon\n",
    "ds_TREFHT_ecearth_land_hist_1_mean=ds_TREFHT_ecearth_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_2_mean=ds_TREFHT_ecearth_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_3_mean=ds_TREFHT_ecearth_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:]\n",
    "ds_TREFHT_ecearth_land_hist_mean=xr.concat([ds_TREFHT_ecearth_land_hist_1_mean,ds_TREFHT_ecearth_land_hist_2_mean,ds_TREFHT_ecearth_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ctl_1_mean=ds_TREFHT_ecearth_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_2_mean=ds_TREFHT_ecearth_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_3_mean=ds_TREFHT_ecearth_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ctl_mean=xr.concat([ds_TREFHT_ecearth_land_ctl_1_mean,ds_TREFHT_ecearth_land_ctl_2_mean,ds_TREFHT_ecearth_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_sust_1_mean=ds_TREFHT_ecearth_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_2_mean=ds_TREFHT_ecearth_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_3_mean=ds_TREFHT_ecearth_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_sust_mean=xr.concat([ds_TREFHT_ecearth_land_sust_1_mean,ds_TREFHT_ecearth_land_sust_2_mean,ds_TREFHT_ecearth_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_ecearth_land_ineq_1_mean=ds_TREFHT_ecearth_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_2_mean=ds_TREFHT_ecearth_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_3_mean=ds_TREFHT_ecearth_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_ecearth_land_ineq_mean=xr.concat([ds_TREFHT_ecearth_land_ineq_1_mean,ds_TREFHT_ecearth_land_ineq_2_mean,ds_TREFHT_ecearth_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_mpiesm.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "da_mask['lat']=ds_TREFHT_mpiesm_hist_1.lat\n",
    "da_mask['lon']=ds_TREFHT_mpiesm_hist_1.lon\n",
    "\n",
    "ds_TREFHT_mpiesm_land_hist_1_mean=ds_TREFHT_mpiesm_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_2_mean=ds_TREFHT_mpiesm_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_3_mean=ds_TREFHT_mpiesm_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_hist_mean=xr.concat([ds_TREFHT_mpiesm_land_hist_1_mean,ds_TREFHT_mpiesm_land_hist_2_mean,ds_TREFHT_mpiesm_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ctl_1_mean=ds_TREFHT_mpiesm_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_2_mean=ds_TREFHT_mpiesm_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_3_mean=ds_TREFHT_mpiesm_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ctl_mean=xr.concat([ds_TREFHT_mpiesm_land_ctl_1_mean,ds_TREFHT_mpiesm_land_ctl_2_mean,ds_TREFHT_mpiesm_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_sust_1_mean=ds_TREFHT_mpiesm_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_2_mean=ds_TREFHT_mpiesm_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_3_mean=ds_TREFHT_mpiesm_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_sust_mean=xr.concat([ds_TREFHT_mpiesm_land_sust_1_mean,ds_TREFHT_mpiesm_land_sust_2_mean,ds_TREFHT_mpiesm_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_mpiesm_land_ineq_1_mean=ds_TREFHT_mpiesm_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_2_mean=ds_TREFHT_mpiesm_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_3_mean=ds_TREFHT_mpiesm_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_mpiesm_land_ineq_mean=xr.concat([ds_TREFHT_mpiesm_land_ineq_1_mean,ds_TREFHT_mpiesm_land_ineq_2_mean,ds_TREFHT_mpiesm_land_ineq_3_mean],'time')\n",
    "\n",
    "mask_dir='/dodrio/scratch/projects/2022_200/project_output/bclimate/sdeherto/wam2layer/scripts/'\n",
    "mask_cesm=xr.open_dataset(mask_dir+'landmask_cesm.nc')\n",
    "da_mask=mask_cesm['landmask']\n",
    "\n",
    "ds_TREFHT_land_hist_1_mean=ds_TREFHT_hist_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_2_mean=ds_TREFHT_hist_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_3_mean=ds_TREFHT_hist_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_hist_mean=xr.concat([ds_TREFHT_land_hist_1_mean,ds_TREFHT_land_hist_2_mean,ds_TREFHT_land_hist_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ctl_1_mean=ds_TREFHT_ctl_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_2_mean=ds_TREFHT_ctl_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_3_mean=ds_TREFHT_ctl_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ctl_mean=xr.concat([ds_TREFHT_land_ctl_1_mean,ds_TREFHT_land_ctl_2_mean,ds_TREFHT_land_ctl_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_sust_1_mean=ds_TREFHT_sust_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_2_mean=ds_TREFHT_sust_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_3_mean=ds_TREFHT_sust_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_sust_mean=xr.concat([ds_TREFHT_land_sust_1_mean,ds_TREFHT_land_sust_2_mean,ds_TREFHT_land_sust_3_mean],'time')\n",
    "\n",
    "\n",
    "ds_TREFHT_land_ineq_1_mean=ds_TREFHT_ineq_1.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_2_mean=ds_TREFHT_ineq_2.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_3_mean=ds_TREFHT_ineq_3.where(da_mask).mean(['lat','lon']).resample(time='AS').mean('time')[-31:-1]\n",
    "ds_TREFHT_land_ineq_mean=xr.concat([ds_TREFHT_land_ineq_1_mean,ds_TREFHT_land_ineq_2_mean,ds_TREFHT_land_ineq_3_mean],'time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a84b9cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WBGTmonmax change to preindustrial')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##make boxplot per esm and case use color coding for cases\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.set_ylim(-1,3)\n",
    "ax2.set_ylim(-0.28,3.78)\n",
    "ax.set_xlim(0.5,6.5)\n",
    "\n",
    "\n",
    "list_futctl_histctl=[ds_TREFHT_land_ctl_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ctl_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ctl_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futineq_futctl=[ds_TREFHT_land_ineq_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_ineq_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_ineq_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "list_futsust_futctl=[ds_TREFHT_land_sust_mean.values-ds_TREFHT_land_hist_mean.values,ds_TREFHT_mpiesm_land_sust_mean.values-ds_TREFHT_mpiesm_land_hist_mean.values,ds_TREFHT_ecearth_land_sust_mean.values-ds_TREFHT_ecearth_land_hist_mean.values]\n",
    "\n",
    "data1=[list_futctl_histctl[0],list_futctl_histctl[0]+list_futineq_futctl[0],list_futctl_histctl[0]+list_futsust_futctl[0]]\n",
    "data2=[list_futctl_histctl[1],list_futctl_histctl[1]+list_futineq_futctl[1],list_futctl_histctl[1]+list_futsust_futctl[1]]\n",
    "data3=[list_futctl_histctl[2],list_futctl_histctl[2]+list_futineq_futctl[2],list_futctl_histctl[2]+list_futsust_futctl[2]]\n",
    "\n",
    "\n",
    "def box_plot(data, edge_color, fill_color,pos0):\n",
    "    bp = ax.boxplot(data, positions = [pos0,pos0+2,pos0+4], widths = 0.4,patch_artist=True)\n",
    "    \n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)       \n",
    "        \n",
    "    return bp\n",
    "\n",
    "# first boxplot pair\n",
    "bp1=box_plot(list_futctl_histctl, edge_color='black', fill_color='pink',pos0=1)\n",
    "\n",
    "# second boxplot pair\n",
    "bp2=box_plot(list_futineq_futctl, edge_color='black', fill_color='lightblue',pos0=1.5)\n",
    "\n",
    "# thrid boxplot pair\n",
    "bp3=box_plot(list_futsust_futctl, edge_color='black', fill_color='lightgreen',pos0=2)\n",
    "\n",
    "ax.set_xticks([1.5, 3.5, 5.5])\n",
    "\n",
    "ax.set_xticklabels(['CESM', 'MPI-ESM', 'EC-EARTH'])\n",
    "ax.hlines(y=0,xmin=0,xmax=10,alpha=0.1)\n",
    "#ax2.hlines(y=0,xmin=0,xmax=10,alpha=0.1,color='r')\n",
    "ax.set_ylabel('WBGTmonmax change to histctl')\n",
    "ax.legend([bp1[\"boxes\"][0], bp2[\"boxes\"][0],bp3[\"boxes\"][0]], ['futctl-histctl', 'futineq-histctl','futsust-histctl'])\n",
    "\n",
    "#ax2.set_ylabel('GMT change to futland_ctl')\n",
    "ax2.hlines(y=1.5,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "ax2.hlines(y=2,xmin=0,xmax=10,alpha=0.15,color='black')\n",
    "\n",
    "ax2.set_ylabel('WBGTmonmax change to preindustrial')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ad0da2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1099837238692039,mpiesm 0.3146596703072349,ecearth 0.8702498750516061\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futctl_histctl[0].mean())+',mpiesm '+str(list_futctl_histctl[1].mean())+',ecearth '+str(list_futctl_histctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be80209b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.3272003672912283,mpiesm 0.438719768617716,ecearth 0.9700679124038761\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futineq_futctl[0].mean())+',mpiesm '+str(list_futineq_futctl[1].mean())+',ecearth '+str(list_futineq_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d712b339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cesm 1.1224539807531617,mpiesm 0.3869800439882214,ecearth 0.9081704654235939\n"
     ]
    }
   ],
   "source": [
    "print('cesm '+str(list_futsust_futctl[0].mean())+',mpiesm '+str(list_futsust_futctl[1].mean())+',ecearth '+str(list_futsust_futctl[2].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d07cdf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f678ba7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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